This course provides a comprehensive introduction to the design, implementation, and management of relational databases. It focuses on the relational model, the SQL language, and the lifecycle of data from ingestion (ELT) to business reporting. Students will work primarily with PostgreSQL to solve real-world data challenges using industry-standard tools like DBeaver and VS Code.
Titular Professors
Professors
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Understand and apply the principles of the relational model and data normalization.
- Master SQL for complex data retrieval, aggregation, and transformation.
- Design efficient database schemas that ensure data integrity and minimize redundancy.
- Execute ELT (Extract, Load, Transform) processes to sanitize and prepare raw data for analysis.
- Develop professional reporting standards for data delivery in business environments.
First Part: Foundations & Ingestion (Weeks 1–7)
Focuses on the mechanics of relational systems. Students learn to set up PostgreSQL, define data types, and use the COPY command to import large-scale datasets (Census, NYC Taxi). Key milestones include mastering relational logic (Joins), basic math, and data aggregation (GROUP BY) to extract initial insights from raw tables.
Second Part: Advanced Analytics & Optimization (Weeks 9–15)
Focuses on sophisticated data manipulation. Students master advanced cleaning (text mining and NULL handling), complex logic via Common Table Expressions (CTEs), and statistical partitioning through Window Functions (RANK, LEAD/LAG). The course concludes with database optimization techniques, including the strategic use of Indexes and Views.
The Final Project: Narrative Synthesis (Weeks 12–14)
A capstone experience where students merge disparate sources (IMDB and Financial data) to answer a specific "Data Story." The emphasis shifts from writing queries to interpreting results, requiring students to perform complex data cleaning and present a "So What?" analysis that translates technical output into business evidence.
The following table relates the learning outcomes to the content taught to achieve them:
RA | Syllabus | Contents |
R1 | Understand the world of Databases | Introduction to Databases |
R2 | Understand the Relational Model | Relational Database Modeling |
R3 | Learn the most popular query language for Databases (SQL) | Basic and Advanced SQL |
R4 | Practice with a real-world case project | Project: Data Analysis using IMDB Database |
The evaluation system will be continuous combining several activities to ease the assimilation of knowledge by the student.
The following table shows the percentage of evaluation of each activity based on the final grade:
R1, R2, R3, R4 | Class Activity | 10% |
R1, R2 | Homework | 10% |
R2, R3 | Mid-Term Exam | 30% |
R4 | Project | 20% |
R2, R3 | Final Exam | 30% |
The objectives of the continuous evaluation are the following:
- Progressive learning of the subject and evaluation of the activity
- Evaluation of the knowledge gotten in exams
- Practice the subject with a real world-case database
Artificial Intelligence: it is prohibited the use of Artificial Intelligence tools such as ChatGPT, Gemini, Claude or others. Using AI tool will be considered as cheating and will be sanctioned with a zero. Moreover, the professor will inform the academic director which could be the basis for deciding on additional disciplinary measures.
Retake policy: since this course uses a continous evaluation model, there will be no retake exams.
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- DeBarros, A. (2022). Practical SQL, 2nd Edition: A Beginner’s Guide to Storytelling with Data. No Starch Press.
- PostgreSQL Global Development Group. PostgreSQL Oficial Documentation
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